PROJECT SUMMARY/ABSTRACT - Drug Design Methodologies - Chris Ho, M.D., Ph.D. This SBIR will develop automated software to accurately estimate drug-target binding affinity and selectivity. This capability is crucial to drug discovery, yet remains elusive in all current design tools. Our technology identifies favorable candidates and distinguishes problematic ones that could exhibit side effects due to weak or non-selective, off-target binding. These compounds cause late phase attrition and the extraordinary costs (>$2B) and time (>10 yrs) needed to develop successful drugs. Lifesaving medications are often shelved due to costs and fear of litigation. By de-risking pipelines, we lower costs, and help companies bring more lifesaving treatments to market. Current tools utilize force fields, which are parameterized sets of equations that simulate chemical behavior, to calculate the free energy of receptor binding. However, they all model electrostatic interactions poorly, using simple point charges and Coulomb?s law, which is inadequate. Instead, we utilize the AMOEBA (Atomic Multipole Optimized Energetics for Biomolecular Applications) force field developed at Washington University. AMOEBA employs significantly more rigorous models for electrostatics, utilizing polarizable atomic multipoles to represent electrostatic potentials around the molecule. Our preliminary data and published studies have shown that the AMOEBA force field is much more accurate in predicting drug-receptor binding free energies than current force fields. Unfortunately, the published academic protocol for deriving AMOEBA parameters for a desired drug is far too difficult, time consuming, and technical to be commercially viable. As such, our hypothesis is that this procedure can be automated in a software product and executed by non-experts in one person hour (aside from quantum mechanical calculation time) rather than the 40+ person hours currently required by a computational chemist. This is our first specific aim. Our second aim is to develop automated software to then utilize these parameters to calculate binding free energies against the drug?s receptor target. The lower the receptor binding free energy, the higher the binding affinity and potency of the compound. In Phase II we will extend the software?s functionality to optimize drugs by suggesting chemical modifications that will improve binding affinity to their targets. A market size and forecast report by Grand View Research states that the 2017 US molecular modeling and virtual screening software market is about $800M and growing, in spite of current shortcomings. Our primary clients will be drug discovery groups at both pharmaceutical and biotech companies, as our technology is applicable to both small molecules and biologics. Our business model includes commercialization via software licensing, consulting, contract research, and IP evaluation. By developing this tool, we will accelerate drug development pipelines and diminish costs, providing significant benefit to both researchers as well as patients and clinical outcomes.